What are deep learning analytics solutions
What is machine learning?
What is machine learning?
Every day we generate more data with all of the devices and technologies we use: smartphones, computers, tablets, interconnected devices, etc. All of these devices generate an enormous amount of data. In 2020, a person generated an average of 1.7 MB of data per second. All of this data is stored in digital databases and represents a considerable source of information: this is big data. But without appropriate processing and effective analysis strategies, this amount of data remains just a mass of bytes that can hardly be mastered. This is where machine learning comes into play and makes it possible to use this data.
What is machine learning?
The first algorithms for machine learning were developed in 1950. Machine learning is both a technology and a science (data science): a computer is enabled to go through a learning process without having been programmed for it beforehand. This technology is closely related to artificial intelligence (AI). Its aim is to highlight patterns (statistically relevant repetition patterns) and to derive statistical prognoses from them. In data mining, data is extracted from a large amount of data. This data is the raw material for machine learning to work out patterns for statistical predictions. For this reason, big data (the entirety of all created and stored data) is inextricably linked with machine learning. The more data that is processed to identify trends, the more accurate the predictions.
The computer uses a learning algorithm and bases its analysis and responses on empirical data from the connected database. For business use, machine learning enables the use of data generated by customers or business activities. Artificial intelligence is therefore a major challenge for companies if they want to benefit from it.
There are several types of learning processes. They are classified based on the data available during the learning phase. When the solution to the task at hand is known, the data is said to be tagged. In this case one speaks of supervised learning. Depending on the type of data, whether discrete or continuous, we speak of classification or regression. If the learning takes place step by step and with a reward system for each correctly performed task, “reinforcement learning” is practiced. The most common case of learning is unsupervised learning. Here the search is carried out without labels. Here a result is to be predicted without already knowing the answers.
What can machine learning be used for?
The power and the interesting thing about machine learning lies in the ability to process an enormous volume of data - the human brain would not be able to do this. Industries with large volumes of data need a solution for their processing. They need to extract information from it that they can work with and make decisions based on. The predictive analysis of this data makes it possible to adapt to certain situations. This is what makes machine learning so interesting. Take the financial sector, for example. Machine learning can be used to detect fraudulent activity, contentious behavior, and other key elements in how financial institutions work.
We are generating more and more transaction data. They are also used by companies to contact their customers depending on their buying behavior and the recurring patterns. Our behavior on the Internet, our search queries and pages visited also generate data. Machine learning uses them to identify our interests. This human-free data processing technology gives a huge advantage to companies that want to take advantage of the amount of data they have at their disposal. A human user can hardly use this information because enormous amounts of data have to be processed here. Take, for example, large corporations like Amazon and Google: AI and machine learning are now indispensable components of their processes, as a large flow of usable data is generated here.
More and more data is being generated. Therefore, more and more companies have to integrate this technology into their structures if they want to use the potential of the information they have at their disposal. Let's think about the devices connected via the Internet. We encounter them more and more frequently in our everyday lives. In 2019, more than 8 billion networked devices were in operation, some of which were controlled by voice recognition. As a result, more data was collected about our rhythm of life, our consumer behavior and our habits. By 2020 this number is expected to have increased fivefold. All of this represents a significant amount of information for companies. With machine learning, relevant data can be recognized and used. So it is very clear: it is a central task. Many applications for our modern society are conceivable: facial recognition, autonomous vehicles, robotics, intelligent houses ... The central question is how the potential can be used. This technology is not only suitable for experienced AI developers. Many companies dare the machine learning adventure; they choose ready-to-use solutions tailored to their goals.
How machine learning works
Machine learning takes place on the basis of "experience". The computer collects a large amount of data. He uses these to analyze and predict situations. The aim of this procedure is to let the machine itself create an "internal plan". According to this plan, she can then identify the key elements to work with in a targeted manner. The machine has to "experiment" with various examples and tests in order to get ahead. For this reason, one speaks of “learning” in this context.
To do this, the computer needs data for learning and training. Data acquisition is the basis for machine learning. This is training data ("training data set"). Software and analysis algorithms are also required. After all, an environment is needed for deployment. As a rule, this is a server that is adapted to the required computing power. Different types of learning differ, among other things, in that the answer sought is already known or not, the type of data analyzed, the environment of the data considered and the type of analysis carried out (statistics, comparisons, image recognition, etc.). The learning algorithms differ depending on the task at hand, which in turn requires the corresponding computing power.
The computer learning process usually takes place in two stages. First, the model is developed on the basis of the test data. These data are also known as "observation data". The task to be done is defined, for example: finding a certain element in a photo, recognizing a statistically relevant repetition, reacting to the signal from a sensor. This is the testing or training phase. The next step is the production of the model. New data can further improve this step. Some systems can continue their learning phase during the production phase. However, it must be ensured that feedback on the results achieved is used to further optimize the model and the behavior of the machine. Other systems can continue to learn on their own and become autonomous.
Several factors are important for the quality of learning:
- The number of relevant examples that the computer can access. The higher the number of these examples, the more precisely the data will be analyzed.
- The number of characteristics used to describe the examples. The simpler and more precise they are (size, weight, quantity, speed, etc.), the more precise and faster the analysis will be.
- The quality of the database used. If too much data is missing, it affects the analysis. Incorrect or inaccurate data can also skew the results.
The more consistently these aspects are taken into account, the more precise the forecast algorithm and the more accurate the analysis. As soon as the project for learning for the computer is defined and the databases are ready, you can start with machine learning!
Successful machine learning projects with OVHcloud:
We have always strived to bring technology to all industries. AI and its great potential should not be reserved for large IT groups or large companies. We want to give you the best possible support in tackling your projects with AI and machine learning. Artificial intelligence helps professionals to be more efficient and makes decision-making easier. OVHcloud gives you the tools to help you meet the challenges facing businesses. This includes B. the predictive analysis of data sets. Users of any profile can take advantage of this. We support our customers in developing their own system for using artificial intelligence.
With OVHcloud you can collect and prepare your data: Use our data analytics solutions for this. You can create your machine learning project step by step. Just a few clicks are enough to use your model. Use the tools and frameworks of your choice, such as TensorFlow, PMML or ONNX.
Working with OVHcloud brings you numerous advantages when developing your machine learning project:
- Respect for your data: We are committed to maintaining the confidentiality of your personal data. The sovereignty of your data takes a central place in our corporate philosophy. You can get your data back at any time.
- Computing power : The automation of provisioning and our infrastructures enables us to offer you high computing power at competitive prices.
- Open Source: In the world of data, open source solutions are the most mature and powerful today. OVHcloud attaches great importance to building its offerings on these programs. These include the Apache Hadoop Suite or Apache Spark.
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